On efficient acquisition and recovery methods for certain types of big data

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Big data is characterized in many circles in terms of the three V's - volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.

Original languageEnglish
Title of host publicationManaging Big Data Integration in the Public Sector
PublisherIGI Global
Pages137-147
Number of pages11
ISBN (Electronic)9781466696501
ISBN (Print)1466696494, 9781466696495
DOIs
StatePublished - 1 Jan 2015

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